rstanarm: Bayesian Applied Regression Modeling via Stan

Estimates previously compiled regression models using the 'rstan' package, which provides the R interface to the Stan C++ library for Bayesian estimation. Users specify models via the customary R syntax with a formula and data.frame plus some additional arguments for priors.

Version: 2.17.4
Depends: R (≥ 3.2.0), Rcpp (≥ 0.12.0), methods
Imports: bayesplot (≥ 1.5.0), ggplot2 (≥ 2.2.1), lme4 (≥ 1.1-8), loo (≥ 2.0.0), Matrix (≥ 1.2-13), nlme (≥ 3.1-124), rstan (≥ 2.17.2), rstantools (≥ 1.4.0), shinystan (≥ 2.3.0), stats, survival (≥ 2.40.1), utils
LinkingTo: StanHeaders (≥ 2.17.1), rstan (≥ 2.17.2), BH (≥ 1.65.0), Rcpp (≥ 0.12.0), RcppEigen (≥ 0.3.3.3.0)
Suggests: betareg, data.table (≥ 1.10.0), digest, gridExtra, HSAUR3, knitr (≥ 1.15.1), MASS, mgcv (≥ 1.8-13), rmarkdown, roxygen2, testthat (≥ 1.0.2)
Published: 2018-04-13
Author: Jonah Gabry [aut], Imad Ali [ctb], Sam Brilleman [ctb], Jacqueline Buros Novik [ctb] (R/stan_jm.R), AstraZeneca [ctb] (R/stan_jm.R), Trustees of Columbia University [cph], Simon Wood [cph] (R/stan_gamm4.R), R Core Deveopment Team [cph] (R/stan_aov.R), Douglas Bates [cph] (R/pp_data.R), Martin Maechler [cph] (R/pp_data.R), Ben Bolker [cph] (R/pp_data.R), Steve Walker [cph] (R/pp_data.R), Brian Ripley [cph] (R/stan_aov.R, R/stan_polr.R), William Venables [cph] (R/stan_polr.R), Paul-Christian Burkner [cph] (R/misc.R), Ben Goodrich [cre, aut]
Maintainer: Ben Goodrich <benjamin.goodrich at columbia.edu>
BugReports: https://github.com/stan-dev/rstanarm/issues
License: GPL (≥ 3)
URL: http://discourse.mc-stan.org, http://mc-stan.org/, http://mc-stan.org/rstanarm/
NeedsCompilation: yes
SystemRequirements: GNU make, pandoc (>= 1.12.3), pandoc-citeproc
Citation: rstanarm citation info
Materials: NEWS
CRAN checks: rstanarm results

Downloads:

Reference manual: rstanarm.pdf
Vignettes: stan_aov: ANOVA Models
stan_betareg: Models for Rate/Proportion Data
stan_glm: GLMs for Binary and Binomial Data
stan_glm: GLMs for Continuous Data
stan_glm: GLMs for Count Data
stan_glmer: GLMs with Group-Specific Terms
stan_jm: Joint Models for Longitudinal and Time-to-Event Data
stan_lm: Regularized Linear Models
stan_polr: Ordinal Models
Hierarchical Partial Pooling
Prior Distributions
How to Use the rstanarm Package
Package source: rstanarm_2.17.4.tar.gz
Windows binaries: r-devel: rstanarm_2.17.4.zip, r-release: rstanarm_2.17.4.zip, r-oldrel: rstanarm_2.17.4.zip
OS X binaries: r-release: rstanarm_2.17.4.tgz, r-oldrel: not available
Old sources: rstanarm archive

Reverse dependencies:

Reverse depends: evidence, nauf
Reverse imports: projpred, psycho, tidyposterior
Reverse suggests: bayesplot, bridgesampling, broom, loo, lsmeans, merTools, RBesT, rstantools, shinystan, sjPlot, sjstats
Reverse enhances: emmeans

Linking:

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